Drug repurposing to identify potential FDA-approved drugs targeting three main angiogenesis receptors through a deep learning framework.

Journal: Molecular diversity
Published Date:

Abstract

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; hence, capillary sprouting occurs from parental vessels, a process known as angiogenesis. Although multiple cellular pathways have been identified, controlling them with one single biomolecule as a multi-target inhibitor could be an attractive strategy for reducing medication side effects. Three critical pathways in angiogenesis have been identified, which are activated by the vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), and epidermal growth factor receptor (EGFR). This study aimed to develop a methodology to discover multi-target inhibitors among over 2000 FDA-approved drugs. Hence, a novel ensemble approach was employed, comprising classification and regression models. First, three different deep autoencoder classifications were generated for each target individually. The top 100 trained models were selected for the high-throughput virtual screening step. After that, all identified molecules with a probability of more than 0.9 in more than 70% of the models were removed to ensure accurate consideration in the regression step. Since the ultimate aim of virtual screening is to discover molecules with the highest success rate in the pharmaceutical industry, various aspects of the molecules in different assays were considered by integrating ten different regression models. In conclusion, this paper contributes to pharmaceutical sciences by introducing eleven diverse scaffolds and eight approved drugs that can potentially be used as inhibitors of angiogenesis receptors, including VEGFR, FGFR, and EGFR. Considering three target receptors simultaneously is another central concept and contribution used. This concept could increase the chance of success, while reducing the possibility of resistance to these agents.

Authors

  • Mohammadreza Torabi
    Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran.
  • Soroush Sardari
    Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.
  • Alejandro Rodríguez-Martínez
    Computer Engineering Department, Bioinformatics and High-Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), 30107, Murcia, Spain.
  • Nooshin Arabi
    Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • Horacio Pérez-Sánchez
    Computer Science Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, E30107, Spain.
  • Fahimeh Ghasemi
    Department of Bioelectric and Biomedical engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Hezar-Jerib Ave, Isfahan, 81746 73461, IR, Iran.